Solved – How resNet increasing the dimension

conv-neural-networkdeep learningmachine learningresidual-networks

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In the above image, It is the part of the resNet Architecture, here they have used dotted line to increase the dimension, but my question is How they are increasing the dimension?? or this dotted line is just a convolution layer to increase the dimension?

Best Answer

That essentially means either linear skip connection, or padding $\mathbf{x}$ (input to the residual block) to appropriate shape.

Note that in equation $(2)$ of the ResNet paper:

$$\textbf{y} = \mathcal{F}(\textbf{x}, W_i) + W_s\textbf{x}$$

You can have $W_s$ mapping $\textbf{x}$ to the desired space.

Here is the excerpt on this from the paper:

When the dimensions increase (dotted line shortcuts in Fig. 3), we consider two options:

(A) The shortcut still performs identity mapping, with extra zero entries padded for increasing dimensions. This option introduces no extra parameter;

(B) The projection shortcut in Eqn.(2) is used to match dimensions (done by 1×1 convolutions).
For both options, when the shortcuts go across feature maps of two sizes, they are performed with a stride of 2.